Learning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection

• URL: http://arxiv.org/abs/2505.16029v1
• Date: Wed, 21 May 2025 21:18:26 GMT
• Title: Learning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection
• Authors: Shichao Li, Peiliang Li, Qing Lian, Peng Yun, Xiaozhi Chen,
• Abstract summary: We build an offboard auto-labeling system that reconstructs pedestrian trajectories from LiDAR point cloud and multi-view images.<n>Our approach significantly improves the 3D pedestrian tracking performance towards higher auto-labeling efficiency.
• Score: 14.56852056332248
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Perceiving pedestrians in highly crowded urban environments is a difficult long-tail problem for learning-based autonomous perception. Speeding up 3D ground truth generation for such challenging scenes is performance-critical yet very challenging. The difficulties include the sparsity of the captured pedestrian point cloud and a lack of suitable benchmarks for a specific system design study. To tackle the challenges, we first collect a new multi-view LiDAR-camera 3D multiple-object-tracking benchmark of highly crowded pedestrians for in-depth analysis. We then build an offboard auto-labeling system that reconstructs pedestrian trajectories from LiDAR point cloud and multi-view images. To improve the generalization power for crowded scenes and the performance for small objects, we propose to learn high-resolution representations that are density-aware and relationship-aware. Extensive experiments validate that our approach significantly improves the 3D pedestrian tracking performance towards higher auto-labeling efficiency. The code will be publicly available at this HTTP URL.

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